arXiv:2605.26496v1 Announce Type: new Abstract: The Mixture of Experts MoE architecture is highly promising for resource constrained on device deployments yet training these models from scratch incurs prohibitive costs Current methods attempt to alleviate this by upcycling dense models into MoEs however they often introduce parameter redundancy that degrades inference efficiency Alternatively standard layer pruning mitigates redundancy but inevitably compromises model accuracy To resolve this dilemma we propose Dense2MoE a novel framework that unifies pruning and upcycling through Layer Fusion
The increasing demand for powerful yet efficient large language models (LLMs) on resource-constrained devices makes optimizing their deployment a critical priority, pushing innovation in model architecture and training. This research addresses the immediate challenge of making advanced AI more accessible and practical beyond data centers.
This development allows for more powerful AI to run directly on devices, reducing reliance on cloud infrastructure, enhancing privacy, and potentially lowering operational costs for AI integration. It is crucial for democratizing advanced AI capabilities and enabling new applications requiring immediate, local AI processing.
The ability to efficiently deploy sophisticated LLMs on-device without significant accuracy loss changes the landscape for edge AI applications, making high-performance AI less reliant on robust internet connectivity or expensive data center resources. This unifies pruning and upcycling through Layer Fusion.
- · Edge AI device manufacturers
- · On-device LLM developers
- · Consumers of AI-powered mobile/IoT devices
- · Companies seeking to reduce cloud AI costs
- · Cloud-centric AI service providers without edge solutions
- · Developers focused solely on large data center models
- · Traditional dense model deployment strategies
More capable and efficient 'on-device' AI models become widely available, improving user experience and opening new application domains.
Reduced data transmission to the cloud for AI inference leads to enhanced privacy and potentially lower latency for AI interactions.
The proliferation of powerful edge AI could shift computing paradigms, decreasing overall reliance on centralized cloud infrastructure for certain AI tasks and impacting the economics of cloud providers.
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Read at arXiv cs.LG